Tox21 Enricher: Web-based Chemical/Biological Functional Annotation Analysis Tool Based on Tox21 Toxicity Screening Platform

Per-and polyfluoroalkyl substances and disrupted sleep: mediating roles of proteins

Background

Per-and polyfluoroalkyl substances (PFAS) contamination may disrupt sleep through disrupted metabolic and immune functions. The study aims to investigate the association and potential mechanism between PFAS and sleep.

Methods

We included 136 young adults recruited between 2014-2018 and 76 were re-assessed between 2020-2022. Additional 8 participants only had complete data between 2020-2022. Plasma PFAS (PFOS, PFOA, PFHxS, PFHpS, PFPeS, PFNA, PFDA) were measured at both visits using liquid-chromatography high-resolution mass spectrometry. Plasma proteins were measured by Olink® Explore 384 Cardiometabolic and Inflammation Panel I. Sleep duration was self-reported at both visits along with follow-up sleep disturbance and sleep-related impairment using validated instruments. We utilized multiple linear regression to explore the association between individual PFAS (in tertile) and these sleep outcomes. PFAS associated with sleep outcomes were subjected to computational toxicology analysis using the Comparative Toxicogenomics Database and Toxicology in the 21st Century database to identify potential genetic links between them. Mediation analysis using proteomic data was then performed to confirm the findings from computational toxicology analysis.

Results

At baseline, one tertile increase in PFDA was associated with 0.39 (95 % CI: 0.05, 0.73) hours of shorter nightly sleep duration, and, at follow-up, PFHxS and PFOA were associated with 0.39 (95 % CI: 0.05, 0.72) and 0.32 (95 % CI: 0.01, 0.63) hours shorter sleep duration, respectively. One tertile increase in PFOS exposure was associated with a 2.99-point increase in sleep disturbance scores (95 % CI: 0.67, 5.31) and a 3.35-point increase in sleep-related impairment scores (95 % CI: 0.51, 6.20). Computational toxicology and mediation analyses identified potential mediating roles for several proteins in the PFAS-sleep associations, including 11-beta-dehydrogenase isozyme 1 (HSD11B1), cathepsin B (CTSB) and several immune system-related proteins.

Conclusion

Future large scale epidemiological and mechanistic studies should confirm our findings and test effect measure modification of the associations by age.

Corrigendum: Tox21Enricher-Shiny: an R Shiny application for toxicity functional annotation analysis

[This corrects the article DOI: 10.3389/ftox.2023.1147608.].

Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

BACKGROUND

Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

OBJECTIVES

Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

METHODS

We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

RESULTS

Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

CONCLUSIONS

Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Critical assessment and integration of separate lines of evidence for risk assessment of chemical mixtures

Humans are exposed to multiple chemicals on a daily basis instead of to just a single chemical, yet the majority of existing toxicity data comes from single-chemical exposure. Multiple factors must be considered such as the route, concentration, duration, and the timing of exposure when determining toxicity to the organism. The need for adequate model systems (in vivo, in vitro, in silico and mathematical) is paramount for better understanding of chemical mixture toxicity. Currently, shortcomings plague each model system as investigators struggle to find the appropriate balance of rigor, reproducibility and appropriateness in mixture toxicity studies. Significant questions exist when comparing single-to mixture-chemical toxicity concerning additivity, synergism, potentiation, or antagonism. Dose/concentration relevance is a major consideration and should be subthreshold for better accuracy in toxicity assessment. Previous work was limited by the technology and methodology of the time, but recent advances have resulted in significant progress in the study of mixture toxicology. Novel technologies have added insight to data obtained from in vivo studies for predictive toxicity testing. These include new in vitro models: omics-related tools, organs-on-a-chip and 3D cell culture, and in silico methods. Taken together, all these modern methodologies improve the understanding of the multiple toxicity pathways associated with adverse outcomes (e.g., adverse outcome pathways), thus allowing investigators to better predict risks linked to exposure to chemical mixtures. As technology and knowledge advance, our ability to harness and integrate separate streams of evidence regarding outcomes associated with chemical mixture exposure improves. As many national and international organizations are currently stressing, studies on chemical mixture toxicity are of primary importance.

The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

BACKGROUND

Most chemicals in commerce have not been evaluated for their carcinogenic potential. The de facto gold-standard approach to carcinogen testing adopts the 2-y rodent bioassay, a time-consuming and costly procedure. High-throughput in vitro assays are a promising alternative for addressing the limitations in carcinogen screening.

OBJECTIVES

We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in vitro gene expression assays.

METHODS

We generated a large toxicogenomics resource comprising [Formula: see text] expression profiles corresponding to 330 chemicals profiled in HepG2 (human hepatocellular carcinoma cell line) at multiple doses and replicates. Predictive models of carcinogenicity and genotoxicity were built using a random forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external sources, including Drugmatrix and the Connectivity Map.

RESULTS

Among profiles with sufficient bioactivity, our classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, was not significantly associated with long-term carcinogenicity in doses up to [Formula: see text]. However, sufficient bioactivity was necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed pathways consistent with known pathways that drive cancer, including DNA damage and repair. The data is available at https://clue.io/CRCGN_ABC , and a portal for query and visualization of the results is accessible at https://carcinogenome.org .

DISCUSSION

We demonstrated an in vitro screening approach using gene expression profiling to predict carcinogenicity and infer MoAs of chemical perturbations. https://doi.org/10.1289/EHP3986.